Abstract
Accuracy of predicting protein secondary structure and solvent accessibility from sequence information has been improved significantly by using information contained in multiple sequence alignments as input to a neural 'network system. For the Asilomar meeting, predictions for 13 proteins were generated automatically using the publicly available prediction method PHD. The results confirm the estimate of 72% three‐state prediction accuracy. The fairly accurate predictions of secondary structure segments made the tool useful as a starting point for modeling of higher dimensional aspects of protein structure. © 1995 Wiley‐Liss, Inc.
| Original language | English |
|---|---|
| Pages (from-to) | 295-300 |
| Number of pages | 6 |
| Journal | Proteins: Structure, Function and Bioinformatics |
| Volume | 23 |
| Issue number | 3 |
| DOIs | |
| State | Published - Nov 1995 |
| Externally published | Yes |
Keywords
- automatic prediction of protein secondary structure and solvent accessibility
- neural networks
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